Recommending media content to a user based on information associated with a referral source

ABSTRACT

Systems and methods for recommending media content to a user based on information associated with a referral source that referred the user to a media item provided by a source of the media content are presented. In one or more aspects, a system is provided that includes a presentation component that presents, via user a interface, a first media item associated with a media presentation source referred to a user through a referral source. The system further includes an analytics component that identifies a second media item based on media items associated with the media presentation source that are referred to other users through the referral source, and a recommendation component that recommends the second media item to the user through the user interface.

TECHNICAL FIELD

This application generally relates to systems and methods forrecommending media content to a user based on information associatedwith a referral source that referred the user to a media item providedby a source of the media content.

BACKGROUND

The proliferation of available streaming content is increasing atexponential levels that will soon reach many millions if not billions ofsuch viewable streaming content. Conventionally, broadcast media hasbeen provided by television or cable channels that typically have beenprovided by a relatively small number of content providers. However,with the ubiquitous nature of media creation and publishing tools,individuals are able to become productive content creators. This hasresulted in exponential growth of available streaming content as well asavailable channels for streaming content. Although users enjoy aplethora of viewing options associated with available streaming content,the task of searching through this sea of content to find items ofinterest is becoming increasingly difficult. Accordingly, mechanisms forautomatically identifying and recommending content that may be ofinterest to a user can greatly enhance user browsing and entertainmentwatching experience.

BRIEF DESCRIPTION OF THE DRAWINGS

Numerous aspects, embodiments, objects and advantages of the presentinvention will be apparent upon consideration of the following detaileddescription, taken in conjunction with the accompanying drawings, inwhich like reference characters refer to like parts throughout, and inwhich:

FIG. 1A illustrates an example system for recommending media content toa user based on information associated with a referral source thatreferred the user to a media item provided by a source of the mediacontent, in accordance with various aspects and embodiments describedherein;

FIGS. 1B-1D present example user interfaces generated/employed byrecommendation systems described herein to present and organize mediaitems in accordance with various aspects and embodiments;

FIG. 2 illustrates another example system for recommending media contentto a user based on information associated with a referral source thatreferred the user to a media item provided by a source of the mediacontent, in accordance with various aspects and embodiments describedherein;

FIG. 3 illustrates another example system for recommending media contentto a user based on information associated with a referral source thatreferred the user to a media item provided by a source of the mediacontent, in accordance with various aspects and embodiments describedherein;

FIG. 4 illustrates another example system for recommending media contentto a user based on information associated with a referral source thatreferred the user to a media item provided by a source of the mediacontent, in accordance with various aspects and embodiments describedherein;

FIG. 5 illustrates another example system for recommending media contentto a user based on information associated with a referral source thatreferred the user to a media item provided by a source of the mediacontent, in accordance with various aspects and embodiments describedherein;

FIG. 6 is a flow diagram of an example method for recommending mediacontent to a user based on information associated with a referral sourcethat referred the user to a media item provided by a source of the mediacontent, in accordance with various aspects and embodiments describedherein;

FIG. 7 is a flow diagram of another example method for recommendingmedia content to a user based on information associated with a referralsource that referred the user to a media item provided by a source ofthe media content, in accordance with various aspects and embodimentsdescribed herein;

FIG. 8 is a flow diagram of another example method for recommendingmedia content to a user based on information associated with a referralsource that referred the user to a media item provided by a source ofthe media content, in accordance with various aspects and embodimentsdescribed herein;

FIG. 9 is a flow diagram of another example method for recommendingmedia content to a user based on information associated with a referralsource that referred the user to a media item provided by a source ofthe media content, in accordance with various aspects and embodimentsdescribed herein;

FIG. 10 is a schematic block diagram illustrating a suitable operatingenvironment in accordance with various aspects and embodiments.

FIG. 11 is a schematic block diagram of a sample-computing environmentin accordance with various aspects and embodiments.

DETAILED DESCRIPTION

The innovation is described with reference to the drawings, wherein likereference numerals are used to refer to like elements throughout. In thefollowing description, for purposes of explanation, numerous specificdetails are set forth in order to provide a thorough understanding ofthis innovation. It may be evident, however, that the innovation can bepracticed without these specific details. In other instances, well-knownstructures and components are shown in block diagram form in order tofacilitate describing the innovation.

By way of introduction, the subject matter described in this disclosurerelates to systems and methods for recommending media content to a userbased on information associated with a referral source that referred theuser to a media item provided by a source of the media content.Networked systems that employ website platforms for content distributionoften present relevant content to users based on certain signals orindicators of related to preferences. These systems often attempt togenerate such signals/indicators by requesting information from theusers via user profiles/accounts. For example, a user can generate aprofile or account that includes information related to the user's age,gender, location, interests, etc. The information provided in a userprofile/account can then be used to identify content relevant to theuser and to recommend this content to the user.

In addition to or in the alternative of using signals associated withuser profiles/accounts, the disclosed systems look directly at areferring source to identify signals that can be employed to determineor infer relevant content for a user. For example, often times, links tovideos available at a media presentation source are provided at anothersource, such as a social media source, an educational informationsource, or a business transaction source. When a user clicks on the linkfrom one of these sources, (referred to herein as the referral source),the user will be brought to the media presentation source to view orplay the video represented by the link. The disclosed systems andmethods can recommend other videos to the user that are provided by themedia presentation source based at least in part on informationassociated with the referral source that provided the link to the video.For example, the disclosed systems can examine other videos available bythe media presentation source that have links at the referral source togenerate a list of “More videos from Referral Source.” The disclosedsystems can further identify those videos having links at the referralsource that were clicked on (e.g., an action referred to herein as“following a link” or “click-through”) by a user of the referral sourceto generate a list of “More videos other users found interesting fromthe Referral Source.” In yet another example, the disclosed systems cananalyze interests of users of the referral source based on their viewinghistory at the media presentation source to identify videos that relateto interests of users from the referral source.

In one or more aspects, a system is provided that includes apresentation component that presents, via user a interface, a firstmedia item associated with a media presentation source referred to auser through a referral source. The system further includes an analyticscomponent that identifies a second media item based on media itemsassociated with the media presentation source that are referred to otherusers through the referral source, and a recommendation component thatrecommends the second media item to the user through the user interface.

In another aspect, a method is disclosed that includes presenting, viauser a interface, a first media item associated with a mediapresentation source referred to a user through a referral source,identifying a second media item based on media items associated with themedia presentation source that are referred to other users through thereferral source, and recommending the second media item to the userthrough the user interface.

Further provided is a tangible computer-readable storage mediumcomprising computer-readable instructions that, in response toexecution, cause a computing system to perform various operations. Theseoperations can include presenting, via user a interface, a first mediaitem associated with a media presentation source referred to a userthrough a referral source, identifying a set of media items based oninformation regarding other users associated with the referral source,and recommending the set of media items to the user through the userinterface.

Referring now to the drawings, with reference initially to FIG. 1A,presented is diagram of an example system 100 for recommending mediacontent to a user based on information associated with a referral sourcethat referred a user to a media item provided by a source of the mediacontent, in accordance with various aspects and embodiments describedherein. Aspects of systems, apparatuses or processes explained in thisdisclosure can constitute machine-executable components embodied withinmachine(s), e.g., embodied in one or more computer readable mediums (ormedia) associated with one or more machines. Such components, whenexecuted by the one or more machines, e.g., computer(s), computingdevice(s), virtual machine(s), etc. can cause the machine(s) to performthe operations described.

System 100 includes a media provider 102, one or more referral sources114, and one or more client devices 118. System 100 also includes one ormore networks 116 connecting the clients 118, referral sources 114 andmedia provider 102. In an aspect, the media provider 102 can include arecommendation platform 120 configured to facilitate recommending mediacontent to a user based on information associated with a referral sourcethat referred the user to the media provider in association with alinked media item. In other aspects, (not shown), the recommendationplatform 120 can be located externally from the media provider andaccessed by the media provider over a network (e.g., a network 116).Recommendation platform 120 can include memory 112 for storing computerexecutable components and instructions. Recommendation platform 120 canfurther include a processor 110 to facilitate operation of theinstructions (e.g., computer executable components and instructions) bythe recommendation platform 120.

A media provider 102 can include an entity that provides media contentto a user via a network 116 (e.g., the Internet). As used herein theterm media content or media item can include but is not limited tostreamable media (e.g., video, live video, video advertisements, music,music videos, sound files and etc.) and static media (e.g., pictures,thumbnails). In an aspect, a media provider 102 can employ one or moreserver computing devices to store and deliver media content to usersthat can be accessed using a browser. For example, media provider 102can provide and present content to a user via a website. In an aspect,media provider 102 is configured to provide streamed media to users overa network 116. The media can be stored in memory 112 and/or at variousservers employed by media provider 102 and accessed via a client device118 using a website platform of the media provider 102. For example,media provider 102 can include a media presentation source that hasaccesses to thousands to billions (and potentially an unlimited number)of shared media (e.g., video and/or audio) files. The media presentationsource can further stream these media files to one or more users atrespective client devices of the one or more users over a network 116.

A referral source 114 can include an information source accessible tousers via a network 116 and configured to provide a link or hyperlink tomedia content provided by media provider 102. For example, referralsource 114 can include a networked content source that providesinformation to a user via a website and includes embedded links at thewebsite to content provided by media provider 102. Example referralsources can include a social networking services website, a website thatcompiles photos and/or links to information found interesting to usersat various other websites, a website that provides news articles andnews multimedia to users, a website that complies photos, a website thatprovides written reviews of places and things, a website that providesproducts for purchasing, or a website that provides educational servicesand information. It should be appreciated that the types of referralsources described above are merely exemplary and that system 100 (andadditional systems described herein) can be employed with a vast arrayof referral sources.

The term link or hyperlink refers to an object or item that serves as areference to remote data, such as media content provided by mediaprovider 102. Hyperlinks are used by networked computing entities (e.g.,media provider 102 and referral sources 114) to link any information toany other information over a network (e.g., the Internet). A link orhyperlink can be presented to a user as text, an image, a thumbnail, orany object that is representative of the data it refers to. Links orhyperlinks to media items (e.g., videos) provided by media source 102can be included at a referral source in various formats (e.g., inclinelinks, anchor links, hyperlinks in hypertext markup language (HTML),hyperlinks in extensible markup language (XML), etc.).

A link can be selected by a user to present the user with the datarepresented by the link. The process of selecting a link by a user isreferred to herein as “following” a link. The effect of following a linkmay vary with the hypertext system used to generate the link and maysometimes depend on the link itself. For instance, on the World WideWeb, most hyperlinks cause the target object to replace the object beingdisplayed, but some are marked to cause the target document to open in anew window. Another possibility is transclusion, for which the linktarget is a document fragment that replaces the link anchor within thesource document. Not only users browsing information at a referralsource can follow hyperlinks. For example, hyperlinks can be followedautomatically by programs. A program that traverses the hypertext,following each hyperlink and gathering all the retrieved documents isknown as a Web spider or crawler.

In an aspect, a link can represent a media item (e.g., a video) andinclude the media item as embedded content at a referral source 114. Forexample, a referral source 114 can include a link to a video provided bymedia provider 102 where the link includes an embedded thumbnail imageof the video. Selection of the embedded thumbnail image can result inthe playing of the video at the referral source 114, via an embeddedplayer, as streamed from the media provider 102. In another aspect, alink can represent a media item (e.g., a video) whereby selection of thelink brings the user to the networked location or source of the mediaitem (e.g., a location of a website employed by the media provider) toconsume the media item.

In an aspect, content associated with media provider 102 (e.g., mediacontent) and a referral source is provided to a user at a client device118 over a network 116. A client device 118 can include any suitablecomputing device associated with a user and configured to interact withmedia provider 102, referral source 114 and/or recommendation platform120. For example, client device 118 can include a desktop computer, alaptop computer, a television, a mobile phone, a tablet personalcomputer (PC), or a personal digital assistant PDA. As used in thisdisclosure, the terms “content consumer” or “user” refer to a person,entity, system, or combination thereof that employs system 100 (oradditional systems described in this disclosure) using a client device118. Network(s) 116 can include wired and wireless networks, includingbut not limited to, a cellular network, a wide area network (WAD, e.g.,the Internet), a local area network (LAN), or a personal area network(PAN). For example, a client device 118 can communicate with a referralsource 114 and media provider 102 (and vice versa) using virtually anydesired wired or wireless technology, including, for example, cellular,WAN, wireless fidelity (Wi-Fi), Wi-Max, WLAN, and etc. In an aspect, oneor more components of system 100 are configured to interact viadisparate networks. For example, a client device 118 can access andreceive media from media provider 102 over a LAN while the mediaprovider 102 communicates with a remote recommendation platform 120 (notdepicted) over a WAN.

In order to facilitate recommending media content to a user based oninformation associated with a referral source 114 that referred the userto a media item provided by media provider 102, recommendation platform120 can include presentation component 104, analytics component 106, andrecommendation component 108.

Presentation component 104 is configured to present media items,provided by media provider 102, to a user via a user interfaceassociated with the media provider 102. For example, media provider 102can be a media presentation source configured to present and distributemedia content (e.g., streaming videos) to users over a network 116.Presentation component 104 can generate and/or employ a user interfacethat facilitates organizing and presenting media content provided bymedia provider 102. The presentation component 202 can generate thisinterface and present the media items in various forms and arrangements.

FIGS. 1B-1D present example user interfaces generated/employed bypresentation component 104 to present and organize media items inaccordance with various aspects and embodiments described herein. Withreference to FIG. 1B, an example user interface 130 can include varioussections such as a primary display section 148 in which a selected videois played/presented in a video player, a subsection having views ofrecommended channels or videos considered relevant to a user 150, achannel guide 144 that can be expanded to display various categories ofchannels, a “More From” section 146 that can be expanded or minimized todisplay videos and/or channels associated with a referral source, and asection with a streaming advertisement 142. The interface 130 can alsoinclude various menu options presented in an upper panel of theinterface including a search box 132, a browse box 134, a movies box136, an upload box 138 and sign in box 140. In some aspects videosand/or channels presented to a user in the “More From” section 146and/or recommended channels or videos section 148 can be displayed asthumbnails or in a list view. A thumbnail can include a static image ofa media item that represents the media item and allows the user toselect and/or preview the media item. In an aspect, a user can select avideo for viewing by selecting a thumbnail view of a video and the videocan be presented to the user in the primary display section 148 or alarger window of a new interface display page (as compared to the sizeof the thumbnail view).

In an aspect, presentation component 104 is configured to present amedia item to a user in the primary display section 148 using a mediaplayer associated with the user interface in response to selection of alink or hyperlink for the media item where the link or hyperlink for themedia item is located at a referral source. For example, with referenceto FIG. 1B, when a user follows a link located at a referral source ABCrepresenting a video entitled “Under the Hood” provided by mediaprovider 102, the presentation component 104 can display the video tothe user in a user in a primary display section 148 of an interface 130associated with the media provider 102. In an aspect, presentationcomponent 104 can begin automatically playing the video. In anotheraspect, the presentation component 104 can present the user with astatic thumbnail view of the video which the user can then select toinitiate playing of the video. Thus, in an aspect, when a user follows alink or hyperlink from a referral source for a video provided by mediaprovider 102, presentation component 104 can facilitate presenting thevideo to the user at an interface (e.g., a website) employed by themedia provider 102. The presentation component 104 can further beconfigured to present various features of media provider 102 via theuser interface such as various media items recommended to the user,search tools for finding media items, menu options that facilitatenavigating content provided by media provider, etc.

Referring back to FIG. 1A, analytics component 106 is configured toidentify one or more media items provided by media provider 102 having arelationship with a referral source at which a link to a media itemprovided by the media provider 102 was located and followed by a user.In particular, when a user follows a link for a media item from areferral source 114 and the media item is provided by media provider102, the analytics component 106 identifies the media item as a mediaitem referred to the user from the referral source 114. The analyticscomponent 106 can then identify one or more other media items providedby media provider 102 having a relationship with the referral source114.

The analytics component 106 can identify a relationship between a mediaitem provided by media provider 102 and a referral source 114 based onvarious features associated with the referral source 114 itself as wellas users of referral source 114. In an aspect, analytics component 106can consider a media item provided by media provider 102 as having arelationship with a referral source 114 if the media item has a link orhyperlink at the referral source 114. For example, analytics component104 can identify all videos provided my media provider 102 having linksat referral source ABC. In another aspect, the analytics component 106can identify other referral sources related to referral source ABC andidentify media items provided by media provider 102 that have linksposted at the other referral sources. For example, where referral sourceABC is an educational program web site related to advanced biology, theanalytics component 106 can identify other referral sources related toadvanced biology and identify media items provided by media provider 102having links at the other related referral sources.

In another aspect, the analytics component 106 can consider a media itemprovided by media provider 102 as having a relationship with a referralsource 114 if a user of the referral source 114 interacted with themedia item (e.g., viewed, watched, liked, commented on, shared, saved,subscribed to, etc.) in some manner at the media provider 102 (e.g.,using an interface associated with media provider 102). For example,analytics component 106 can identify all videos provided by mediaprovider 102 that users of referral source ABC watched and/or marked orindicated as liked. In yet another aspect, analytics component 106 canconsider a media item provided by media provider 102 as having arelationship with a referral source 114 if the media item includescontent related to content associated with the referral source 114. Forexample, where the referral source 114 is an educational program websiterelated to advanced biology, the analytics component 106 can identifyother videos provided by media provider 102 related to advanced biology.

The analytics component 106 can further apply various filters toorganize, filter, and refine lists of media items provided by mediaprovider identified as having some type of relationship with a referralsource 114 that referred a user to a media item provided by mediaprovider 102 (e.g., via a hyperlink located at the referral source). Forexample, the analytics component 106 can generate a first list all mediaitems provided by media provider 102 that have links at a particularreferral source 114 from which a media item of media provider 102 wasreferred to a user. The analytics component 106 can further filter thefirst list based on those media items included in the first list thatwere actually selected and followed by other users of the referralsource. In another aspect, the analytics component 106 can generate afirst list of media items that were watched by other users associatedwith a referral source 114 from which a media item of media provider 102was referred to a user. The analytics component 102 can further filterthe first list of media items as a function of a known associationbetween the user and a subset of the other users. For example, theanalytics component 106 can identify all videos in the first list thatwere watched by users selected as or considered friends with the user.This filtered list can be associated with a titles such as “Other VideosYour Friends whom Also Use Referral Source [insert name of referralsource] Watched.”

In another aspect, the analytics component 106 can filter media itemsbased on information associated with a link for a media item at areferral source that a user clicked on which brought the user to mediaprovider 102 to view the media item. In particular, links or hyperlinkscan include rich data or metadata (e.g., attribution tags) thatindicates various attributes associated with the link that can beemployed by analytics component 106 to filter and organize media itemsin a manner that tailors the media items to the user. For example, linksor hyperlinks can include information associated with the media itemrepresented by the link, the client device 118 that formatted andcreated the link, location of the client device 118 that formatted andcreated the link, time of creation of the link, and information aboutthe user who created the link.

For example, the analytics component 106 can examine a hyperlinkrepresentative of a media item provided by media provider 102 that auser followed from a referral source and identify a location associatedwith the user device employed to generate the link. According to thisexample, the hyperlink can include location information which theanalytics component 106 can examine to identify the location. Forinstance, the location information can include an internet protocol (IP)address associated with the client device at the time of generation ofthe hyperlink at the referral source. The analytics component 106 canthen filter a list of media items identified as having a relationshipwith the referral source 114 based on the location. For example, where auser selects a link to a video at a referral source 114 that was createdby a client device 118 in France, the analytics component 106 canidentify videos provided by media provider having a relationship to thereferral source 114 that are from (e.g., uploaded to media provider byusers in France) or otherwise associated with France (e.g., watched byusers located in France, having audio in French, etc.).

In another example, the analytics component 106 can examine a hyperlinkrepresentative of a media item provided by media provider 102 that afirst user followed from a referral source 114 and identify a seconduser that posted the hyperlink at the referral source 114. The analyticscomponent 106 can then identify other videos provided by media provider102 that the second user posted links to at the referral source 114 orother videos the second user posted links to at other referral sources.In yet another example, the analytics component 106 can examine ahyperlink representative of a first media item provided by mediaprovider 102 that a user followed from a referral source 114 andidentify a time when the link was posted at the referral source 114. Theanalytics component 106 can then identify other videos provided by mediaprovider 102 that have links posted at the referral source 114 within apredetermined time frame associated with the time which the link to thefirst media item was posted.

In an aspect, the analytics component 106 can employ a ranking component(discussed supra with respect to FIG. 2) to facilitate filtering andorganizing media items identified based on a relationship of the mediaitems with a referral source 114. For example, the ranking component canapply various indicators of user interest in media items identified bythe analytics component 106 to rank the media items as a function ofestimated user interest level in the respective media items. Theanalytics component 106 can then filter a set of media items based onranking information respectively associated with media items in the set.

The recommendation component 108 is configured to recommend one or moremedia items identified by the analytics component 106 to a user througha user interface (e.g., interfaces 130, 160 and 170 respectivelypresented in FIGS. 1B, 1C and 1D) generated by presentation component104. In an aspect, the recommendation component 108 can recommend allmedia items identified by analytics component 106. Depending on thevarious filters applied by the analytics component 106, the referralsource 114, and the media item that was referred by the referral source,this list of recommended media items can vary greatly in size. In someaspects to narrow down the list of recommended media items to thoseconsidered most interesting or relevant to a user, the recommendationcomponent 108 can recommend media items to the user as a function ofranking (discussed supra with respect to FIG. 2).

In an aspect, the presentation component 104 is configured to presentmedia items collected or identified by recommendation component 108 inone or more recommended videos sections of a user interface associatedwith media provider 102. For example, the recommendation component 108can recommend one or more videos identified by analytics component 106as videos the user may have an interest in watching. According to thisexample, the presentation component 104 can present these one or morerecommended videos to a user in a recommendation section of a userinterface as thumbnails or in a list view.

In an aspect, a recommendation section can be tailored to present onlyvideos recommended based at least in part on a relationship with areferral source 114 from which a media item provided by media provider102 was recommended to a user. According to this aspect, thepresentation component 104 or recommendation component 108 can associatea title with a recommend video section that describes the basis forwhich the videos were recommended. For example, the recommendationsection can be titled “More Videos From [insert name of referralsource],” and include videos that have links at the referral source. Inanother example, the recommendation section can be titled “More VideosUsers of [insert name of referral source] Watched,” and include videosprovided by media provider that users of the referral source watched. Inanother example, the recommendation section can be titled “More VideosUsers of [insert name of referral source] Followed,” and include videosprovided by media provider having links at the referral source thatusers of the referral source clicked on or selected.

In an another aspect, the recommended videos can be included in arecommendation section or other section that includes videos recommendedor presented to the user for various reasons that are not based on arelationship with a referral source. According to this aspect, a videowhich is recommended to the user based at least in part on arelationship with a referral source can be identified by thepresentation component 104 with the addition of a tag, overlay or otherinsignia to the thumbnail view of the video that indicates the basis forwhich the video was recommended. For example, the videos recommendedbased on click through from a referral source can be associated withtext that states “Followed from [insert name of referral source here].”

For example, FIGS. 1B-1D depict example user interfaces with sectionsentitled “More From” and “Recommended Channels or Videos.” The “MoreFrom” section 146 can be configured to include videos recommended to auser by recommendation component 108 based at least in part on anassociation with a referral source. The “Recommended Channels or Videos”section 150 can be configured to include videos recommended to a userfor various reasons not limited to or restricted by a relatedness to areferral source. For example, as seen in FIGS. 1B and 1C, the “MoreFrom” section 146 can include more videos from referral source ABC inresponse to selection of a link to a video (the video entitled “Underthe Hood” being presented to the user in the primary display area 148)at referral source ABC that is provided by media provider 102. Inanother example, the “More From” section include media items recommendedto a user that are considered popular with users of referral source ABC,as seen in interface 170 of FIG. 1D.

In an aspect, the presentation component 104 can present the “More From”section in a minimized view, as seen in interface 130 of FIG. 1B. A usercan further select to expand the “More From” section to view therecommended media items in a list view or thumbnail view 152 as depictedin interface 160 of FIG. 1C.

FIG. 1D, further exemplifies an interface 170 in which recommendedvideos are included in the “Recommended Channels or Videos” section 150for various reasons and where videos or channels that are recommendedbased on a relationship with a referral source are identified. Forexample, Video 11 is identified with a label indicating that the videowas posted by user “John” at referral source ABC. According to thisaspect, video 11 can be recommended based in part on the fact that thereferral video “Under the Hood” was also posted by John at referralsource ABC. Video 12 can be recommended for another reason having nobasis with respect to an association with referral source ABC, whilechannel 9 is recommended and identified as being followed by users ofreferral source ABC.

Referring back to FIG. 1A, presentation component 104 can presentcontent viewing options for use with any suitable type of deviceconfigured to interface with a streaming media provider, for examplemobile phone, tablet computer, desktop computer, server system, personalcomputers, cable set top box, satellite set top box, cable modem,television set, internet-enabled televisions, television computer devicemedia extender device, video cassette recorder device, blu-ray device,DVD (digital versatile disc or digital video disc) device, compact discdevice, video game system, audio/video receiver, radio device, portablemusic player, navigation system, car stereo, etc.

The respective devices listed above (and additional devices suitable forinterfacing with a streaming media provider) often have differentcapabilities and limitations (e.g., screen size, decoders . . . ). In anaspect, presentation component 104 can provide presentation options inaccordance with different device capabilities or limitations. Forexample, data rendering capabilities may be more limited in a mobiledevice (e.g., a smart-phone) than in a fixed computing device (e.g., adesktop computer), more effort may be required of a user to consumecontent such as a video (or other information) from the mobile devicethan would be required of the user in viewing the same video from afixed computing device. In addition, because displays of various mobiledevices are often smaller than displays in fixed computing devices, itmay be possible only to display a relatively small amount of informationat any given time on a mobile device. Finally, data connections betweena mobile device and various networked resources (e.g., the Internet) maybe slower than corresponding data connections between a fixed computingdevice and the same networked resources. Accordingly, presentationcomponent 104 can generate user options to account for variations indevice functionality and available bandwidth for consumption andrendering of media content.

In view of the above, presentation component 104 can present content invarious formats and/or in accordance with various display mediums. Inparticular, the presentation component 104 can adapt and optimizedisplay of options and content based on respective client devices. Forexample, presentation component 104 can adapt the manner in which avideo recommended for re-watch is presented to a user (e.g., as anend-cap, as a pop up, in a recommendation section, etc.) based on clientdevice 118 capabilities and display restrictions. In another example,presentation component 202 can present a section of video in a formatsuch as H.263, H.264 AVC, MPEG-4 SP, VP8, or other suitable format basedon the client device 118. In yet another example, presentation component104 can present an audio of a video in formats such as for example AACLC/LTP, HE-AACv1(AAC+), HE-AACv2 (enhanced AAC+), AMR-NB, AMR-WB, FLAC,MP3, MIDI, Vorbis, PCM/WAVE, etc.

In an aspect, presentation component 104 can automatically configure orpresent user options to consume video based on encoding parameters suchas video resolution, video frame rate, video bit rate, video codec,audio codec, audio channels, audio bit rate, etc. Thus presentationcomponent 104 can choose a format to consume content that best suitscapabilities of specific consumption mediums, available bandwidth, filesize, processing capabilities, screen resolution, screen size, availableencoders, available decoders, etc.

Referring now to FIG. 2, presented is diagram of another example system200 for recommending media content to a user based on informationassociated with a referral source that referred the user to a media itemprovided by a source of the media content, in accordance with variousaspects and embodiments described herein. System 200 includes samefeatures and functionalities of system 200 with the addition of rankingcomponent 202. Repetitive description of like elements employed inrespective embodiments of systems and interfaces described herein areomitted for sake of brevity.

Recommendation platform 120 can include a ranking component 202 tofacilitate identifying media items by analytics component 106 forpotential recommendation to users by recommendation component. Theranking component 202 is configured to apply ranking information torespective media items of a set of media items identified by analyticscomponent 106. The set of media items identified by the analyticscomponent 106 includes one or more media items identified as having arelationship with a referral source 106 that referred a user to a mediaitem provided by media provider 102 in the various manners discussedsupra. The respective ranking information associated with the mediaitems can represent a determined or inferred probability that the userhas interest in accessing (e.g., viewing, watching, listening to,reading, etc.) the respective media items.

In an aspect, the analytics component 106 can employ the rankinginformation associated with a media item to further filter a set ofmedia items into a subset of media items that are tailored to the user.The recommendation component 108 can then recommend the subset of mediaitems to the user. In another aspect, the ranking information associatedwith a media item can influence the manner in which the recommendationcomponent 108 recommends the media item to the user and/or how thepresentation component 104 presents the media item to the user. Inanother aspect, the ranking associated with a media item can influencehow the media item appears in a search query result for a search queryissued by the user. Still in yet another aspect (discussed supra withrespect to FIG. 5), the ranking associated with a media item caninfluence association of advertisements with the media item, chargingschemes for the advertisements, and data collection regarding userconsumption/interaction with the advertisements.

For example, the ranking component 202 can apply respective rankings toa set of videos identified by analytics component 106 as videos relatedto a referral source 114 that referred a video provided by mediaprovider 102 to a user (e.g., via a hyperlink located at the referralsource 114 that was followed by the user). The rankings can representprobabilities that the user has interest in watching the respectivevideos. The ranking associated with a particular video can furtherinfluence identification of the video by analytics component 106 forinclusion in a subset (e.g., one or more videos) of videos forrecommendation to the user by recommendation component 108. For example,the analytics component 106 can identify a subset of media items basedon rankings of the media items in the subset being above a predeterminedthreshold. The basis for the rankings can vary as a function of userinterest factors discussed below. Thus filtering or identification ofmedia items by the analytics component 106 can be a function of thevarious user interest factors discussed below. For example, media itemswatched by other users having similar interests to the user for whichmedia items are being recommended, (an example user interest factor),can receive higher rankings than other videos watched by users havingdissimilar interests. Thus filtering or identification of media items bythe analytics component 106 can be a function of ranking which is afunction of the various user interest factors discussed below. Thereforea ranking associated with a particular video can influence whether thevideo is recommended to the user.

A ranking associated with a particular video can also influence how thevideo is recommended to a user (e.g., placement/order of the video in alist of recommended videos). For example, the recommendation component108 can recommend a video having a higher ranking above a having a lowerranking. In another example, the recommendation component 108 can chooseto recommend only videos that have a ranking above a predeterminedthreshold. In yet another example, where the ranking is above anotherpredetermined threshold, the recommendation component 108 can place thevideo at the top of a recommendation list or an auto-play component(discussed supra with respect to FIG. 4) could initiate automaticplaying of the video.

A ranking associated with a media item by ranking component 202 can alsoinfluence the order in which the video appears in a search query resultfor a search based on one or more factors associated with the video. Aranking associated with a media item can also influence advertisementassociation with the media item and/or charging for advertisementsassociated with the media item. It should be appreciated that manner inwhich a ranking associated with a media item, as assigned by the subjectranking component 202 affects filtering of media items by analyticscomponent 106, recommendation of media items by recommendation component108, presentation of recommended media items by presentation component104, ordering of recommended media items, search query inclusion andordering of media items, auto-replay of media items, and/or advertisingassociated with media items can vary and is not limited to the aboveexamples.

The ranking component 202 is configured to rank media items identifiedby analytics component 106 as a function of one or more user interestfactors considered reflective of a user's interest the media item. Theseuser interest factors can be grouped into three categories includingfactors that relate to popularity of a media item, factors that relateto relationships between users and factors that relate to content of amedia item.

In an aspect, the ranking component 202 can consider factors that arereflective of popularity of media items and associate higher rankingswith media items that are more popular than media items considered lesspopular amongst a group of users. For example, the analytics component106 can identify a set of videos provided by media provider 102 thathave links at a referral source 114 that referred a user to a videoprovided by media provider 102. The ranking component 202 can then rankthose videos in the set as a function of popularity of those videosamongst other users. According to this example, the ranking component202 can consider videos included in the set that were collectivelywatched the most by the other users as most popular and rank thosevideos higher than videos that were watched the least. In addition, theranking component 202 can consider factors reflective of popularity of amedia item referred to and followed by a user when ranking other mediaitems related to the referral source 114 for recommendation to the user.For example, where a user followed an unpopular video, the rankingcomponent 202 can associate lower rankings to videos related to thereferral source that were identified by analytics component 106.

Various factors/indicators can be considered reflective of popularity ofa media item. In an aspect popularity of a media item is reflective ofwhether the media item is considered interesting to other users. Forexample, such indicators can include but are not limited to: number ofusers who viewed a media item, number of users who liked a media item,number of users who subscribed to a media item or channel associatedwith the media item, degree of interaction of users with a media item,number of users who created links to the media items a particularreferral source, number of users that created links to the media item atmultiple referral sources, number of existing links to the media item atcollective referral sources, and number of users that selected a linkfor the media item at a referral source (e.g., followed the media itemlink at a referral source 114 to view the media item at an interfaceassociated with media provider 102 that provides the media item). Otherexample of popularity of a media item can relate to recency of activitywith the media item including but not limited to, recency of creation ofa link or links to the media item at a referral source, and frequency ofuser interaction with a media item within a window of time consideredrecent (e.g., past 24 hours, past week, past month, etc.), includinginteraction at the media provider 102 and interaction at a referralsource 114 (e.g., selection of a link to the media item).

The ranking component 202 can also consider factors reflective ofrelationships between users when ranking media items. For example, theranking component 202 can associate higher rankings with media itemswatched or interacted with by users that share similarities to a firstuser who was referred to a media item by a referral source 114 thanmedia items watched or interacted with by users who are dissimilar tothe first user. The recommendation component 108 can then recommendvideos having a higher ranking to first user. This feature follows theassumption that like users will generally have similar interests inmedia content.

For example, the analytics component 106 can identify a set of videosprovided by media provider 102 that other users from a referral source114 that referred a first user to a media item, provided by the mediaprovider, watched, liked, enjoyed, etc. The ranking component 202 canthen examine the other users respectively associated with the videos ofthe set based on various factors including but not limited to: userpreferences, topics of interest to the users, user demographics, andsocial relationships between the other users and the first user (e.g.,whether any of the other users are friends of the first user). Theranking component 202 can further examine information associated withthe first user including but not limited to, first user preferences,topics of interest to the first user, and first user demographics. Theranking component 202 can then rank then rank the videos in the setbased on similarities between the first user and other usersrespectively associated with the videos. The ranking component 202 canfurther rank media items identified by analytics component 106 afunction of popularity of the media items within a cluster of usersclustered based on similarities or social associations to the referreduser.

The ranking component 202 can also consider factors reflective of thecontent associated with a referred media item and/or referral source 114when ranking media items to reflect predicted user interest in the mediaitems. For example, the analytics component 106 can identify a set ofvideos provided by media provider 102 that have links at a referralsource 114, and/or are associated with users of the referral source,that referred a user to a video provided by media provider 102. Theranking component 202 can then identify content associated with thereferred video and rank the videos in the set based on similarity incontent to the referred video. For example, where the referred video isabout horse jumping, the ranking component 202 can rank videos in theset related to horse jumping higher than videos related to horsegrooming. In another aspect, the ranking component 202 can analyze thereferral source to identify content associated with the referral sourceand rank the videos in the set based on similarity in content betweenthe referral source and the videos in the set. For example, where thereferral source is a home improvement information website, the rankingcomponent 202 can rank videos in the set related to home improvementhigher than videos that are not related to home improvement.

The ranking component 202 can employ various algorithms (stored inmemory 112 or external memory accessible to ranking component 202) thatapply one or more user interest factors, discussed above, consideredreflective of a user's interest in media items, to rank media itemshaving a relationship with a referral source 114. In an aspect, theanalytics component 106 can generate a list or set of media items havinga relationship with a referral source 114 based the variousfilters/parameter discussed above with respect to FIG. 1A, system 100.The ranking component 202 can then rank the media items included in theset based on the various user interest factors discussed above. Forexample, the analytics component 106 can generate a set of media itemshaving links at a referral source that were selected/followed by usersof the referral source. The ranking component 202 can then apply one ormore algorithms that account for one or more of the user interestfactors discussed below to rank the media items of the subset.

In another aspect, the ranking component 202 can apply one or more ofthe various filters discussed above with respect to the analyticscomponent 106 in FIG. 1A, system 100 (e.g., click through, locationinformation associated with the media item hyperlink, etc.), as well asone or more of the user interest factors discussed above, to rank mediaitems identified by analytics component 106 as having a relationshipwith a referral source. For example, the analytics component 106 canidentify all videos provided by media provider 102 that have links atreferral source ABC. The ranking component 202 can then rank the mediaitem as a function of one or more of the user interest factors discussedand one or more of the above and one or more of the filters discussedabove with respect to the analytics component 106 in FIG. 1A, system 100(e.g., click through, location information associated with the mediaitem hyperlink, etc.). According to this aspect, any of the filtersdiscussed above with respect to analytics component 106 in FIG. 1A canbe considered user interest factors.

FIG. 3 presents a diagram of another example system 300 for recommendingmedia content to a user based on information associated with a referralsource that referred the user to a media item provided by a source ofthe media content, in accordance with various aspects and embodimentsdescribed herein. System 300 includes same features and functionalitiesof system 200 with the addition of inference component 302. Repetitivedescription of like elements employed in respective embodiments ofsystems and interfaces described herein are omitted for sake of brevity.

Inference component 302 is configured to provide for or aid in variousinferences or determinations associated with aspects presentationcomponent 104, analytics component 106, recommendation component 108 andranking component 202. In an aspect, all or portions of recommendationplatform 120 can be operatively coupled to inference component 302.Moreover, inference component 302 may be granted access to all orportions of recommendation platform 102, media provider 102, externalreferral sources 114, and client devices 117.

In an aspect, the inference component 302 can facilitate inferring aranking for a video by ranking component 202 (e.g., inferring aprobability that the user has an interest in the video). For example,the inference component 302 can apply one or more of the above factorsto media items, such as videos, identified by analytics component 106,to infer a degree/probability of user interest in the respective mediaitems. The inference component 302 can further infer whether and how torecommend and present media items based on ranking informationassociated therewith. For example, the inference component 302 can inferwhether to recommend a video to a user based on the ranking associatedtherewith. In another example, the inference component 302 can infer howto order a video recommended to a user amongst other videos recommendedto the user in a recommendation section of a user interface based inpart on a ranking associated therewith.

In order to provide for or aid in the numerous inferences describedherein, inference component 302 can examine the entirety or a subset ofthe data to which it is granted access and can provide for reasoningabout or infer states of the system, environment, etc. from a set ofobservations as captured via events and/or data. An inference can beemployed to identify a specific context or action, or can generate aprobability distribution over states, for example. The inference can beprobabilistic—that is, the computation of a probability distributionover states of interest based on a consideration of data and events. Aninference can also refer to techniques employed for composinghigher-level events from a set of events and/or data.

Such an inference can result in the construction of new events oractions from a set of observed events and/or stored event data, whetheror not the events are correlated in close temporal proximity, andwhether the events and data come from one or several event and datasources. Various classification (explicitly and/or implicitly trained)schemes and/or systems (e.g., support vector machines, neural networks,expert systems, Bayesian belief networks, fuzzy logic, data fusionengines, etc.) can be employed in connection with performing automaticand/or inferred action in connection with the claimed subject matter.

A classifier can map an input attribute vector, x=(x1, x2, x3, x4, xn),to a confidence that the input belongs to a class, such as byf(x)=confidence(class). Such classification can employ a probabilisticand/or statistical-based analysis (e.g., factoring into the analysisutilities and costs) to prognose or infer an action that a user desiresto be automatically performed. A support vector machine (SVM) is anexample of a classifier that can be employed. The SVM operates byfinding a hyper-surface in the space of possible inputs, where thehyper-surface attempts to split the triggering criteria from thenon-triggering events. Intuitively, this makes the classificationcorrect for testing data that is near, but not identical to trainingdata. Other directed and undirected model classification approachesinclude, e.g., naïve Bayes, Bayesian networks, decision trees, neuralnetworks, fuzzy logic models, and probabilistic classification modelsproviding different patterns of independence can be employed.Classification as used herein also is inclusive of statisticalregression that is utilized to develop models of priority.

FIG. 4 presents a diagram of another example system 400 for recommendingmedia content to a user based on information associated with a referralsource that referred the user to a media item provided by a source ofthe media content, in accordance with various aspects and embodimentsdescribed herein. System 400 includes same features and functionalitiesof system 300 with the addition of auto-play component 402. Repetitivedescription of like elements employed in respective embodiments ofsystems and interfaces described herein are omitted for sake of brevity.

Auto-play component 402 is configured to automatically replay a mediaitem, such as a video or audio track, in response recommendation of themedia item by recommendation component. For example, in response toidentification by the analytics component 106 and/or recommendationcomponent 108 of a video that a user may have an interest in based atleast in part on a relationship between the video a referral source 114,the auto-play component 402 can automatically play the video in a mediaplayer of a user interface at a client device 118 employed by the user.In an aspect, the auto-play component 402 can be configured toautomatically play a media item in response to a determination/inferenceby ranking component 202 and/or inference component 302 that the mediaitem has a high probability of interest to the user. According to thisaspect, the auto-play component 402 can be configured to automaticallyreply a video or audio track in response to association of a rankingwith the video or audio track that is above a predetermined threshold.For example, the auto-play component 402 can be configured toautomatically replay videos that have a 90% relevance level to the user(e.g., based on the various factors discussed herein).

In an aspect, the auto-play component 402 is configured to initiate theautomatic replay of a video or audio track as a function of user contextor user preferences. According to this aspect, the inference component302 can infer a user context and/or user preferences. In other aspects,user preferences can be provided to recommendation platform 120 by user.User context can relate information including but not limited to, ausers' physical location, a users' physical surroundings, a users'current activities, and time of day. For example, where therecommendation component 108 identifies a sports match video as having ahigh probability of relevance for a particular user, the auto-playcomponent 402 can choose to automatically replay the video when the useris travelling to attend a similar sports match as opposed to a time whenthe user is attending church.

FIG. 5 presents a diagram of another example system 500 for recommendingmedia content to a user based on information associated with a referralsource that referred the user to a media item provided by a source ofthe media content, in accordance with various aspects and embodimentsdescribed herein. System 500 includes same features and functionalitiesof system 400 with the addition of advertising component 502. Repetitivedescription of like elements employed in respective embodiments ofsystems and interfaces described herein are omitted for sake of brevity.

Advertising component 502 is configured to provide advertisements tousers. These advertisements can include video ads, text ads, pictureads, audio ads, etc. In some aspects, the advertisements are associatedwith media content recommended to a user. In other aspects, a media itemrecommended to a user is an advertisement.

In an aspect, advertisement component 602 can identify advertisementsbest suited for association with a media item recommended to a userbased on a relationship of the media item with a referral source. Forexample, the analytics component 106 can identify a second media itemassociated with a media provider 102 based in part on a relationshipbetween the second media item and a referral source 114 that referred afirst media item, provided by the media provider 102, to a user. Therecommendation component 108 can further recommend the media item to theuser. In an aspect, the advertisement component 602 can analyze therelationship between the second media item and the referral source 114and infer (e.g., using inference component 302) an advertisement toassociated with the second media item based on the referral source.

For example, the advertisement component 602 can identify the referralsource and infer advertisements that related to content associated withthe referral source. In another example, the advertisement component 602can examiner users of the referral source and identify advertisementsthat art targeted to the users of the referral source (e.g., based onuser interests, user demographics, user location, etc.).

In another aspect, the advertising component 602 can prioritizeadvertisement placement with media items recommended to a user based inpart on association of the media items with referral sources. Accordingto this aspect, recommendation platform 120 can receive and/or identifyinformation regarding referral sources where links to media itemsprovided by media provider are located, the number of links distributedat various referral sources for the respective media items, the numberof click-through associated with respective links for the respectivemedia items, and the number of user who posted the links. Theadvertisement component 602 can associate advertisement with media itemsbased on one or more of these factors. By associating advertisementswith media items based on one or more of the above listed factors, theadvertisements will not only have a high probability of reaching usersof media provider 102, but users of various referral sources inconnection with usage of the various referral sources by the users.Accordingly, in an aspect, the advertisement component 602 can selecthigh quality advertisements to associate with media items based on oneor more of the above noted factors. The Advertisement component 602 canalso require premium payment for advertisements associated with mediaitems based on one or more of the above noted factors.

In another aspect, the advertising component 602 can prioritizeadvertisement placement with media items recommended to a user based inpart on an inferred/determined user interest level in the media itemreflected by a ranking associated with the media item. For example, whenranking component 202 associates a ranking with a media item thatreflects a high probability the user will view the media item, theadvertising component 602 can choose to place high qualityadvertisements with the recommended media item. Similarly, theadvertising component 602 can choose to associate advertisements thathave been purchased at a higher premium in exchange for premiumplacement with highly ranked media items.

In view of the example systems and/or devices described herein, examplemethods that can be implemented in accordance with the disclosed subjectmatter can be further appreciated with reference to flowcharts in FIGS.6-9. For purposes of simplicity of explanation, example methodsdisclosed herein are presented and described as a series of acts;however, it is to be understood and appreciated that the disclosedsubject matter is not limited by the order of acts, as some acts mayoccur in different orders and/or concurrently with other acts from thatshown and described herein. For example, a method disclosed herein couldalternatively be represented as a series of interrelated states orevents, such as in a state diagram. Moreover, interaction diagram(s) mayrepresent methods in accordance with the disclosed subject matter whendisparate entities enact disparate portions of the methods. Furthermore,not all illustrated acts may be required to implement a method inaccordance with the subject specification. It should be furtherappreciated that the methods disclosed throughout the subjectspecification are capable of being stored on an article of manufactureto facilitate transporting and transferring such methods to computersfor execution by a processor or for storage in a memory.

FIG. 6 illustrates a flow chart of an example method 600 forrecommending media content to a user based on information associatedwith a referral source that referred the user to a media item providedby a source of the media content, in accordance with aspects describedherein. At 602, a first media item associated with a media presentationsource referred to a user through a referral source is presented to theuser via a user interface (e.g., using presentation component 104). At604, a second media item is identified based on media items associatedwith the media presentation source that are referred to other usersthrough the referral source (e.g., using analytics component 106). At606, the second media item is recommended to the user through the userinterface (e.g. using recommendation component 108).

FIG. 7 illustrates a flow chart of another example method 700 forrecommending media content to a user based on information associatedwith a referral source that referred the user to a media item providedby a source of the media content, in accordance with aspects describedherein. At 702, a first media item associated with a media presentationsource referred to a user through a referral source is presented to theuser via a user interface (e.g., using presentation component 104). At704, a set of media items associated with the media presentation sourcethat are referred to other users through the referral source and wereselected by the other users for presentation at the media presentationsource tare identified (e.g., using analytics component 106). At 706,the set of media items are filtered based on similarities between theuser and the other users to generate a subset of media items (e.g.,using analytics component 106). At 708, the second media item isrecommended to the user through the user interface (e.g. usingrecommendation component 108).

FIG. 8 illustrates a flow chart of another example method 800 forrecommending media content to a user based on information associatedwith a referral source that referred the user to a media item providedby a source of the media content, in accordance with aspects describedherein. At 802, a first media item associated with a media presentationsource referred to a user through a referral source is presented to theuser via a user interface (e.g., using presentation component 104). At804, a set of media items is identified based on information regardingother users associated with the referral source (e.g., using analyticscomponent 106). At 806, the set of media items are recommended to theuser through the user interface (e.g., using recommendation component108).

FIG. 9 illustrates a flow chart of another example method 900 forrecommending media content to a user based on information associatedwith a referral source that referred the user to a media item providedby a source of the media content, in accordance with aspects describedherein. At 902, a first media item associated with a media presentationsource referred to a user through a referral source is presented to theuser via a user interface (e.g., using presentation component 104). At904, a set of media items is identified based on information regardingother users associated with the referral source (e.g., using analyticscomponent 106). At 906, the respective media items of the set are rankedbased on factors related to inferred user interest in the respectivemedia items (e.g., using ranking component 202). At 908, a subset of themedia are identified as a function of the ranking (e.g., using analyticscomponent 106). At 910, the subset of the media items are recommended tothe user through the user interface as a function of the ranking (e.g.,using recommendation component 108).

Example Operating Environments

The systems and processes described below can be embodied withinhardware, such as a single integrated circuit (IC) chip, multiple ICs,an application specific integrated circuit (ASIC), or the like. Further,the order in which some or all of the process blocks appear in eachprocess should not be deemed limiting. Rather, it should be understoodthat some of the process blocks can be executed in a variety of orders,not all of which may be explicitly illustrated in this disclosure.

With reference to FIG. 10, a suitable environment 1000 for implementingvarious aspects of the claimed subject matter includes a computer 1002.The computer 1002 includes a processing unit 1004, a system memory 1006,a codec 1005, and a system bus 1008. The system bus 1008 couples systemcomponents including, but not limited to, the system memory 1006 to theprocessing unit 1004. The processing unit 1004 can be any of variousavailable processors. Dual microprocessors and other multiprocessorarchitectures also can be employed as the processing unit 1004.

The system bus 1008 can be any of several types of bus structure(s)including the memory bus or memory controller, a peripheral bus orexternal bus, and/or a local bus using any variety of available busarchitectures including, but not limited to, Industrial StandardArchitecture (ISA), Micro-Channel Architecture (MSA), Extended ISA(EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus(USB), Advanced Graphics Port (AGP), Personal Computer Memory CardInternational Association bus (PCMCIA), Firewire (IEEE 13104), and SmallComputer Systems Interface (SCSI).

The system memory 1006 includes volatile memory 1010 and non-volatilememory 1012. The basic input/output system (BIOS), containing the basicroutines to transfer information between elements within the computer1002, such as during start-up, is stored in non-volatile memory 1012. Inaddition, according to present innovations, codec 1005 may include atleast one of an encoder or decoder, wherein the at least one of anencoder or decoder may consist of hardware, a combination of hardwareand software, or software. Although, codec 1005 is depicted as aseparate component, codec 1005 may be contained within non-volatilememory 1012. By way of illustration, and not limitation, non-volatilememory 1012 can include read only memory (ROM), programmable ROM (PROM),electrically programmable ROM (EPROM), electrically erasableprogrammable ROM (EEPROM), or flash memory. Volatile memory 1010includes random access memory (RAM), which acts as external cachememory. According to present aspects, the volatile memory may store thewrite operation retry logic (not shown in FIG. 10) and the like. By wayof illustration and not limitation, RAM is available in many forms suchas static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM),double data rate SDRAM (DDR SDRAM), and enhanced SDRAM (ESDRAM.

Computer 1002 may also include removable/non-removable,volatile/non-volatile computer storage medium. FIG. 10 illustrates, forexample, disk storage 1014. Disk storage 1014 includes, but is notlimited to, devices like a magnetic disk drive, solid state disk (SSD)floppy disk drive, tape drive, Jaz drive, Zip drive, LS-70 drive, flashmemory card, or memory stick. In addition, disk storage 1014 can includestorage medium separately or in combination with other storage mediumincluding, but not limited to, an optical disk drive such as a compactdisk ROM device (CD-ROM), CD recordable drive (CD-R Drive), CDrewritable drive (CD-RW Drive) or a digital versatile disk ROM drive(DVD-ROM). To facilitate connection of the disk storage devices 1014 tothe system bus 1008, a removable or non-removable interface is typicallyused, such as interface 1016.

It is to be appreciated that FIG. 10 describes software that acts as anintermediary between users and the basic computer resources described inthe suitable operating environment 1000. Such software includes anoperating system 1018. Operating system 1018, which can be stored ondisk storage 1014, acts to control and allocate resources of thecomputer system 1002. Applications 1020 take advantage of the managementof resources by operating system 1018 through program modules 1024, andprogram data 1026, such as the boot/shutdown transaction table and thelike, stored either in system memory 1006 or on disk storage 1014. It isto be appreciated that the claimed subject matter can be implementedwith various operating systems or combinations of operating systems.

A user enters commands or information into the computer 1002 throughinput device(s) 1028. Input devices 1028 include, but are not limitedto, a pointing device such as a mouse, trackball, stylus, touch pad,keyboard, microphone, joystick, game pad, satellite dish, scanner, TVtuner card, digital camera, digital video camera, web camera, and thelike. These and other input devices connect to the processing unit 1004through the system bus 1008 via interface port(s) 1030. Interfaceport(s) 1030 include, for example, a serial port, a parallel port, agame port, and a universal serial bus (USB). Output device(s) 1036 usesome of the same type of ports as input device(s). Thus, for example, aUSB port may be used to provide input to computer 1002, and to outputinformation from computer 1002 to an output device 1036. Output adapter1034 is provided to illustrate that there are some output devices 1036like monitors, speakers, and printers, among other output devices 1036,which require special adapters. The output adapters 1034 include, by wayof illustration and not limitation, video and sound cards that provide ameans of connection between the output device 1036 and the system bus1008. It should be noted that other devices and/or systems of devicesprovide both input and output capabilities such as remote computer(s)1038.

Computer 1002 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1038. The remote computer(s) 1038 can be a personal computer, a server,a router, a network PC, a workstation, a microprocessor based appliance,a peer device, a smart phone, a tablet, or other network node, andtypically includes many of the elements described relative to computer1002. For purposes of brevity, only a memory storage device 1040 isillustrated with remote computer(s) 1038. Remote computer(s) 1038 islogically connected to computer 1002 through a network interface 1042and then connected via communication connection(s) 1044. Networkinterface 1042 encompasses wire and/or wireless communication networkssuch as local-area networks (LAN) and wide-area networks (WAN) andcellular networks. LAN technologies include Fiber Distributed DataInterface (FDDI), Copper Distributed Data Interface (CDDI), Ethernet,Token Ring and the like. WAN technologies include, but are not limitedto, point-to-point links, circuit switching networks like IntegratedServices Digital Networks (ISDN) and variations thereon, packetswitching networks, and Digital Subscriber Lines (DSL).

Communication connection(s) 1044 refers to the hardware/softwareemployed to connect the network interface 1042 to the bus 1008. Whilecommunication connection 1044 is shown for illustrative clarity insidecomputer 1002, it can also be external to computer 1002. Thehardware/software necessary for connection to the network interface 1042includes, for exemplary purposes only, internal and externaltechnologies such as, modems including regular telephone grade modems,cable modems and DSL modems, ISDN adapters, and wired and wirelessEthernet cards, hubs, and routers.

Referring now to FIG. 11, there is illustrated a schematic block diagramof a computing environment 1100 in accordance with this disclosure. Thesystem 1100 includes one or more client(s) 1102 (e.g., laptops, smartphones, PDAs, media players, computers, portable electronic devices,tablets, and the like). The client(s) 1102 can be hardware and/orsoftware (e.g., threads, processes, computing devices). The system 1100also includes one or more server(s) 1104. The server(s) 1104 can also behardware or hardware in combination with software (e.g., threads,processes, computing devices). The servers 1104 can house threads toperform transformations by employing aspects of this disclosure, forexample. One possible communication between a client 1102 and a server1104 can be in the form of a data packet transmitted between two or morecomputer processes wherein the data packet may include video data. Thedata packet can include a metadata, e.g., associated contextualinformation, for example. The system 1100 includes a communicationframework 1106 (e.g., a global communication network such as theInternet, or mobile network(s)) that can be employed to facilitatecommunications between the client(s) 1102 and the server(s) 1104.

Communications can be facilitated via a wired (including optical fiber)and/or wireless technology. The client(s) 1102 include or areoperatively connected to one or more client data store(s) 1108 that canbe employed to store information local to the client(s) 1102 (e.g.,associated contextual information). Similarly, the server(s) 1104 areoperatively include or are operatively connected to one or more serverdata store(s) 1110 that can be employed to store information local tothe servers 1104.

In one embodiment, a client 1102 can transfer an encoded file, inaccordance with the disclosed subject matter, to server 1104. Server1104 can store the file, decode the file, or transmit the file toanother client 1102. It is to be appreciated, that a client 1102 canalso transfer uncompressed file to a server 1104 and server 1104 cancompress the file in accordance with the disclosed subject matter.Likewise, server 1104 can encode video information and transmit theinformation via communication framework 1106 to one or more clients1102.

The illustrated aspects of the disclosure may also be practiced indistributed computing environments where certain tasks are performed byremote processing devices that are linked through a communicationsnetwork. In a distributed computing environment, program modules can belocated in both local and remote memory storage devices.

Moreover, it is to be appreciated that various components described inthis description can include electrical circuit(s) that can includecomponents and circuitry elements of suitable value in order toimplement the embodiments of the subject innovation(s). Furthermore, itcan be appreciated that many of the various components can beimplemented on one or more integrated circuit (IC) chips. For example,in one embodiment, a set of components can be implemented in a single ICchip. In other embodiments, one or more of respective components arefabricated or implemented on separate IC chips.

What has been described above includes examples of the embodiments ofthe present invention. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing the claimed subject matter, but it is to be appreciated thatmany further combinations and permutations of the subject innovation arepossible. Accordingly, the claimed subject matter is intended to embraceall such alterations, modifications, and variations that fall within thespirit and scope of the appended claims. Moreover, the above descriptionof illustrated embodiments of the subject disclosure, including what isdescribed in the Abstract, is not intended to be exhaustive or to limitthe disclosed embodiments to the precise forms disclosed. While specificembodiments and examples are described in this disclosure forillustrative purposes, various modifications are possible that areconsidered within the scope of such embodiments and examples, as thoseskilled in the relevant art can recognize.

In particular and in regard to the various functions performed by theabove described components, devices, circuits, systems and the like, theterms used to describe such components are intended to correspond,unless otherwise indicated, to any component which performs thespecified function of the described component (e.g., a functionalequivalent), even though not structurally equivalent to the disclosedstructure, which performs the function in the disclosure illustratedexemplary aspects of the claimed subject matter. In this regard, it willalso be recognized that the innovation includes a system as well as acomputer-readable storage medium having computer-executable instructionsfor performing the acts and/or events of the various methods of theclaimed subject matter.

The aforementioned systems/circuits/modules have been described withrespect to interaction between several components/blocks. It can beappreciated that such systems/circuits and components/blocks can includethose components or specified sub-components, some of the specifiedcomponents or sub-components, and/or additional components, andaccording to various permutations and combinations of the foregoing.Sub-components can also be implemented as components communicativelycoupled to other components rather than included within parentcomponents (hierarchical). Additionally, it should be noted that one ormore components may be combined into a single component providingaggregate functionality or divided into several separate sub-components,and any one or more middle layers, such as a management layer, may beprovided to communicatively couple to such sub-components in order toprovide integrated functionality. Any components described in thisdisclosure may also interact with one or more other components notspecifically described in this disclosure but known by those of skill inthe art.

In addition, while a particular feature of the subject innovation mayhave been disclosed with respect to only one of several implementations,such feature may be combined with one or more other features of theother implementations as may be desired and advantageous for any givenor particular application. Furthermore, to the extent that the terms“includes,” “including,” “has,” “contains,” variants thereof, and othersimilar words are used in either the detailed description or the claims,these terms are intended to be inclusive in a manner similar to the term“comprising” as an open transition word without precluding anyadditional or other elements.

As used in this application, the terms “component,” “module,” “system,”or the like are generally intended to refer to a computer-relatedentity, either hardware (e.g., a circuit), a combination of hardware andsoftware, software, or an entity related to an operational machine withone or more specific functionalities. For example, a component may be,but is not limited to being, a process running on a processor (e.g.,digital signal processor), a processor, an object, an executable, athread of execution, a program, and/or a computer. By way ofillustration, both an application running on a controller and thecontroller can be a component. One or more components may reside withina process and/or thread of execution and a component may be localized onone computer and/or distributed between two or more computers. Further,a “device” can come in the form of specially designed hardware;generalized hardware made specialized by the execution of softwarethereon that enables the hardware to perform specific function; softwarestored on a computer readable storage medium; software transmitted on acomputer readable transmission medium; or a combination thereof.

Moreover, the words “example” or “exemplary” are used in this disclosureto mean serving as an example, instance, or illustration. Any aspect ordesign described in this disclosure as “exemplary” is not necessarily tobe construed as preferred or advantageous over other aspects or designs.Rather, use of the words “example” or “exemplary” is intended to presentconcepts in a concrete fashion. As used in this application, the term“or” is intended to mean an inclusive “or” rather than an exclusive“or”. That is, unless specified otherwise, or clear from context, “Xemploys A or B” is intended to mean any of the natural inclusivepermutations. That is, if X employs A; X employs B; or X employs both Aand B, then “X employs A or B” is satisfied under any of the foregoinginstances. In addition, the articles “a” and “an” as used in thisapplication and the appended claims should generally be construed tomean “one or more” unless specified otherwise or clear from context tobe directed to a singular form.

Computing devices typically include a variety of media, which caninclude computer-readable storage media and/or communications media, inwhich these two terms are used in this description differently from oneanother as follows. Computer-readable storage media can be any availablestorage media that can be accessed by the computer, is typically of anon-transitory nature, and can include both volatile and nonvolatilemedia, removable and non-removable media. By way of example, and notlimitation, computer-readable storage media can be implemented inconnection with any method or technology for storage of information suchas computer-readable instructions, program modules, structured data, orunstructured data. Computer-readable storage media can include, but arenot limited to, RAM, ROM, EEPROM, flash memory or other memorytechnology, CD-ROM, digital versatile disk (DVD) or other optical diskstorage, magnetic cassettes, magnetic tape, magnetic disk storage orother magnetic storage devices, or other tangible and/or non-transitorymedia which can be used to store desired information. Computer-readablestorage media can be accessed by one or more local or remote computingdevices, e.g., via access requests, queries or other data retrievalprotocols, for a variety of operations with respect to the informationstored by the medium.

On the other hand, communications media typically embodycomputer-readable instructions, data structures, program modules orother structured or unstructured data in a data signal that can betransitory such as a modulated data signal, e.g., a carrier wave orother transport mechanism, and includes any information delivery ortransport media. The term “modulated data signal” or signals refers to asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in one or more signals. By way ofexample, and not limitation, communication media include wired media,such as a wired network or direct-wired connection, and wireless mediasuch as acoustic, RF, infrared and other wireless media.

In view of the exemplary systems described above, methodologies that maybe implemented in accordance with the described subject matter will bebetter appreciated with reference to the flowcharts of the variousfigures. For simplicity of explanation, the methodologies are depictedand described as a series of acts. However, acts in accordance with thisdisclosure can occur in various orders and/or concurrently, and withother acts not presented and described in this disclosure. Furthermore,not all illustrated acts may be required to implement the methodologiesin accordance with certain aspects of this disclosure. In addition,those skilled in the art will understand and appreciate that themethodologies could alternatively be represented as a series ofinterrelated states via a state diagram or events. Additionally, itshould be appreciated that the methodologies disclosed in thisdisclosure are capable of being stored on an article of manufacture tofacilitate transporting and transferring such methodologies to computingdevices. The term article of manufacture, as used in this disclosure, isintended to encompass a computer program accessible from anycomputer-readable device or storage media.

What is claimed is:
 1. A system, comprising: a memory; and a processorthat executes computer executable instructions stored in the memory thatcause the processor to: receive, from a user device, a request topresent a first media item, wherein the request to present the firstmedia item is sent in response to selection of a link associated withthe first media item, by a first user of the user device, displayed at areferral source, wherein the referral source is a first content platformthat is distinct from a media presentation source that is a secondcontent platform; in response to the request to present the first mediaitem, present, via a user interface on the user device, the first mediaitem associated with the media presentation source; identify a group ofusers connected to the first user through the referral source; select asecond user included in the group of users, wherein the second user hasincluded a first link to the first media item in a first socialnetworking post hosted by the referral source; determine that the seconduser has included a second link to a second media item in a secondsocial networking post hosted by the referral source; identify thesecond media item from a plurality of media items associated with themedia presentation source based on-the determination that the seconduser has included the second link to the second media item in the secondsocial networking post; and recommend the second media item to the firstuser through the user interface.
 2. The system of claim 1, wherein thefirst content platform and the second content platform are at least oneof a website or an application.
 3. The system of claim 1, wherein thecomputer executable instructions further cause the processor to identifythe second media item based on a time at which the second socialnetworking post including the second link was posted at the referralsource.
 4. The system of claim 1, wherein the computer executableinstructions further cause the processor to identify the second mediaitem based on a geographical location associated with a device employedto post the first link to the first media item in the first socialnetworking post.
 5. The system of claim 1, wherein the computerexecutable instructions further cause the processor to identify thesecond media item based on information regarding the second user.
 6. Thesystem of claim 5, wherein the computer executable instructions furthercause the processor to select the second user based on a watch historyof the second user at the media presentation source.
 7. The system ofclaim 5, wherein the computer executable instructions further cause theprocessor to select the second user based on demographic similaritybetween the first user and the second user.
 8. The system of claim 5,wherein the computer executable instructions further cause the processorto identify the second media item based on media items the second userfound interesting at the media presentation source.
 9. The system ofclaim 1, wherein the computer executable instructions further cause theprocessor to identify the second media item based on a similarity of thesecond media item to content associated with the referral source.
 10. Amethod comprising: receiving, from a user device, a request to present afirst media item, wherein the request to present the first media item issent in response to selection of a link associated with the first mediaitem, by a first user of the user device, displayed at a referralsource, wherein the referral source is a first content platform that isdistinct from a media presentation source that is a second contentplatform; in response to the request to present the first media item,presenting, via a user interface on the user device, the first mediaitem associated with the media presentation source; identifying, using ahardware processor, a group of users connected to the first user throughthe referral source; selecting a second user included in the group ofusers, wherein the second user has included a first link to the firstmedia item in a first social networking post hosted by the referralsource; determining that the second user has included a second link to asecond media item in a second social networking post hosted by thereferral source; identifying the second media item from a plurality ofmedia items associated with the media presentation source based on thedetermination that the second user has included the second link to thesecond media item in the second social networking post; and recommendingthe second media item to the first user through the user interface. 11.The method of claim 10, wherein the first content platform and thesecond content platform are at least one of a website or an application.12. The method of claim 10, further comprising identifying the secondmedia item based on a time at which the second social networking postincluding the second link was posted at the referral source.
 13. Themethod of claim 10, further comprising identifying the second media itembased on a geographical location associated with a device employed topost the first link to the first media item in the first socialnetworking post.
 14. The method of claim 10, further comprisingidentifying selecting the second user based on a watch history of thesecond user at the media presentation source.
 15. The method of claim10, further comprising selecting the second user based on a similaritybetween the second user and the first user with respect to user interestor user demographics.
 16. The method of claim 10, further comprisingidentifying the second media item based on a similarity of the secondmedia item to content associated with the referral source.
 17. Anon-transitory computer-readable medium having instructions storedthereon that, in response to execution, cause a system including aprocessor to perform operations, comprising: receiving, from a userdevice, a request to present a first media item, wherein the request topresent the first media item is sent in response to selection of a linkassociated with the first media item, by a first user of the userdevice, displayed at a referral source, wherein the referral source is afirst content platform that is distinct from a media presentation sourcethat is a second content platform; in response to the request to presentthe first media item, presenting, via a user interface on the userdevice, the first media item associated with the media presentationsource; identifying a group of users connected to the first user throughthe referral source; selecting a second user included in the group ofusers, wherein the second user has included a first link to the firstmedia item in a first social networking post hosted by the referralsource; determining that the second user has included a second link to asecond media item in a second social networking post hosted by thereferral source; identifying the second media item from a plurality ofmedia items associated with the media presentation source based on thedetermination that the second user has included the second link to thesecond media item in the second social networking post; and recommendingthe second media item to the first user through the user interface. 18.The non-transitory computer-readable medium of claim 17, wherein thefirst content platform and the second content platform are at least oneof a website or an application.